OpenRaiser/Intern-Atlas huggingface dataset:
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Venue: arXiv cs.CV 2023
Year: 2023

Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manner is non-trivial due to the challenges posed by variations in disaster-specific damage, diversity in satellite imagery, and the dearth of extensive, labeled datasets. To circumvent these issues, this paper introduces a human-in-the-loop workflow for rapidly training building damage assessment models after a natural disaster. This article details a case study using this workflow, executed in partnership with the American Red Cross during a tornado event in Rolling Fork, Mississippi in March, 2023. The output from our human-in-the-loop modeling process achieved a precision of 0.86 and recall of 0.80 for damaged buildings when compared to ground truth data collected post-disaster. This workflow was implemented end-to-end in under 2 hours per satellite imagery scene, highlighting its potential for real-time deployment.
1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation
Venue: arXiv cs.CV 2023
Year: 2023

The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) aims to harness the substantial amount of remote sensing data gathered over extensive periods for the monitoring and analysis of Earth's ecosystems'health. The subtask, Multimodal SAR-to-EO Image Translation, involves the use of robust SAR data, even under adverse weather and lighting conditions, transforming it into high-quality, clear, and visually appealing EO data. In the context of the SAR2EO task, the presence of clouds or obstructions in EO data can potentially pose a challenge. To address this issue, we propose the Clean Collector Algorithm (CCA), designed to take full advantage of this cloudless SAR data and eliminate factors that may hinder the data learning process. Subsequently, we applied pix2pixHD for the SAR-to-EO translation and Restormer for image enhancement. In the final evaluation, the team 'CDRL' achieved an MAE of 0.07313, securing the top rank on the leaderboard.
On Addressing the Limitations of Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

This report gives a summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.
Preprocessing Complexity for Some Graph Problems Parameterized by Structural Parameters
Venue: arXiv cs.CC 2023
Year: 2023

Structural graph parameters play an important role in parameterized complexity, including in kernelization. Notably, vertex cover, neighborhood diversity, twin-cover, and modular-width have been studied extensively in the last few years. However, there are many fundamental problems whose preprocessing complexity is not fully understood under these parameters. Indeed, the existence of polynomial kernels or polynomial Turing kernels for famous problems such as Clique, Chromatic Number, and Steiner Tree has only been established for a subset of structural parameters. In this work, we use several techniques to obtain a complete preprocessing complexity landscape for over a dozen of fundamental algorithmic problems.
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and fine-tuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challenges for out-of-distribution (OOD) state actions and often suffers from data inefficiency for training. Despite many efforts being devoted to addressing OOD state actions, the latter (data inefficiency) receives little attention in offline RL. To address this, this paper proposes the cross-domain offline RL, which assumes offline data incorporate additional source-domain data from varying transition dynamics (environments), and expects it to contribute to the offline data efficiency. To do so, we identify a new challenge of OOD transition dynamics, beyond the common OOD state actions issue, when utilizing cross-domain offline data. Then, we propose our method BOSA, which employs two support-constrained objectives to address the above OOD issues. Through extensive experiments in the cross-domain offline RL setting, we demonstrate BOSA can greatly improve offline data efficiency: using only 10\% of the target data, BOSA could achieve {74.4\%} of the SOTA offline RL performance that uses 100\% of the target data. Additionally, we also show BOSA can be effortlessly plugged into model-based offline RL and noising data augmentation techniques (used for generating source-domain data), which naturally avoids the potential dynamics mismatch between target-domain data and newly generated source-domain data.
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
Venue: arXiv cs.IR 2023
Year: 2023

Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their identifiers. So far, much effort has been devoted to developing effective generative retrieval models. There has been less attention paid to the robustness perspective. When a new retrieval paradigm enters into the real-world application, it is also critical to measure the out-of-distribution (OOD) generalization, i.e., how would generative retrieval models generalize to new distributions. To answer this question, firstly, we define OOD robustness from three perspectives in retrieval problems: 1) The query variations; 2) The unforeseen query types; and 3) The unforeseen tasks. Based on this taxonomy, we conduct empirical studies to analyze the OOD robustness of several representative generative retrieval models against dense retrieval models. The empirical results indicate that the OOD robustness of generative retrieval models requires enhancement. We hope studying the OOD robustness of generative retrieval models would be advantageous to the IR community.
Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields
Venue: arXiv cs.CV 2023
Year: 2023

Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.
3D Reconstruction of Spherical Images based on Incremental Structure from Motion
Venue: arXiv cs.CV 2023
Year: 2023

3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to the frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively exploited due to the capability of recording surrounding environments from one camera exposure. Classical 3D reconstruction pipelines, however, cannot be used for spherical images. Besides, there exist few software packages for 3D reconstruction of spherical images. Based on the imaging geometry of spherical cameras, this study investigates the algorithms for the relative orientation using spherical correspondences, absolute orientation using 3D correspondences between scene and spherical points, and the cost functions for BA (bundle adjustment) optimization. In addition, an incremental SfM (Structure from Motion) workflow has been proposed for spherical images using the above-mentioned algorithms. The proposed solution is finally verified by using three spherical datasets captured by both consumer-grade and professional spherical cameras. The results demonstrate that the proposed SfM workflow can achieve the successful 3D reconstruction of complex scenes and provide useful clues for the implementation in open-source software packages. The source code of the designed SfM workflow would be made publicly available.
Relevance-Based Compression of Cataract Surgery Videos
Venue: arXiv cs.MM 2023
Year: 2023

In the last decade, the need for storing videos from cataract surgery has increased significantly. Hospitals continue to improve their imaging and recording devices (e.g., microscopes and cameras used in microscopic surgery, such as ophthalmology) to enhance their post-surgical processing efficiency. The video recordings enable a lot of user-cases after the actual surgery, for example, teaching, documentation, and forensics. However, videos recorded from operations are typically stored in the internal archive without any domain-specific compression, leading to a massive storage space consumption. In this work, we propose a relevance-based compression scheme for videos from cataract surgery, which is based on content specifics of particular cataract surgery phases. We evaluate our compression scheme with three state-of-the-art video codecs, namely H.264/AVC, H.265/HEVC, and AV1, and ask medical experts to evaluate the visual quality of encoded videos. Our results show significant savings, in particular up to 95.94% when using H.264/AVC, up to 98.71% when using H.265/HEVC, and up to 98.82% when using AV1.
Let's Resonate! How to Elicit Improvisation and Letting Go in Interactive Digital Art
Venue: arXiv cs.GR 2023
Year: 2023

Participatory art allows for the spectator to be a participant or a viewer able to engage actively with interactive art. Real-time technologies offer new ways to create participative artworks. We hereby investigate how to engage participation through movement in interactive digital art, and what this engagement can awaken, focusing on the ways to elicit improvisation and letting go. We analyze two Virtual Reality installations, ''InterACTE'' and ''Eve, dance is an unplaceable place,'' involving body movement, dance, creativity and the presence of an observing audience. We evaluate the premises, the setup, and the feedback of the spectators in the two installations. We propose a model following three different perspectives of resonance: 1. Inter Resonance between Spectator and Artwork, which involves curiosity, imitation, playfulness and improvisation. 2. Inner Resonance of Spectator him/herself, where embodiment and creativity contribute to the sense of being present and letting go. 3. Collective Resonance between Spectator/Artwork and Audience, which is stimulated by curiosity, and triggers motor contagion, engagement and gathering. The two analyzed examples seek to awaken open-minded communicative possibilities through the use of interactive digital artworks. Moreover, the need to recognize and develop the idea of resonance becomes increasingly important in this time of urgency to communicate, understand and support collectivity.
Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval
Venue: arXiv cs.HC 2023
Year: 2023

Just as other disciplines, the humanities explore how computational research approaches and tools can meaningfully contribute to scholarly knowledge production. We approach the design of computational tools through the analytical lens of 'human-AI collaboration.' However, there is no generalizable concept of what constitutes 'meaningful' human-AI collaboration. In terms of genuinely human competencies, we consider criticality and reflection as guiding principles of scholarly knowledge production. Although (designing for) reflection is a recurring topic in CSCW and HCI discourses, it has not been centered in work on human-AI collaboration. We posit that integrating both concepts is a viable approach to supporting 'meaningful' human-AI collaboration in the humanities. Our research, thus, is guided by the question of how critical reflection can be enabled in human-AI collaboration. We address this question with a use case that centers on computer vision (CV) tools for art historical image retrieval. Specifically, we conducted a qualitative interview study with art historians and extended the interviews with a think-aloud software exploration. We observed and recorded our participants' interaction with a ready-to-use CV tool in a possible research scenario. We found that critical reflection, indeed, constitutes a core prerequisite for 'meaningful' human-AI collaboration in humanities research contexts. However, we observed that critical reflection was not fully realized during interaction with the CV tool. We interpret this divergence as supporting our hypothesis that computational tools need to be intentionally designed in such a way that they actively scaffold and support critical reflection during interaction. Based on our findings, we suggest four empirically grounded design implications for 'critical-reflective human-AI collaboration'.
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
Venue: arXiv cs.AI 2023
Year: 2023

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover from the performance drop caused by compression. However, the training data is not always available due to privacy issues or other factors. In this work, we present a data-free fine-tuning approach for pruning the backbone of deep neural networks. In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map. Afterwards, the pruned backbone can be combined with the original network head to make predictions. We generate synthetic images by back-propagating gradients to noise images while relying on L1-pruning for the backbone pruning. In our experiments, we show that our approach is task-independent due to pruning only the backbone. By evaluating our approach on 2D human pose estimation, object detection, and image classification, we demonstrate promising performance compared to the unpruned model. Our code is available at https://github.com/holzbock/dfbf.
Towards Exascale CFD Simulations Using the Discontinuous Galerkin Solver FLEXI
Venue: arXiv cs.DC 2023
Year: 2023

Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the high, local computational load that allows for computational efficiency in massively parallel simulations. To this end, the discontinuous Galerkin (DG) framework FLEXI was selected to demonstrate exascale readiness within the Center of Excellence for Exascale CFD (CEEC) by simulating shock buffet on a three-dimensional wing segment at transsonic flight conditions. This paper summarizes the recent progress made to enable the simulation of this challenging exascale problem. For this, it is first demonstrated that FLEXI scales excellently to over 500 000 CPU cores on HAWK at the HLRS. To tackle the considerable resolution requirements near the wall, a novel wall model is proposed that takes compressibility effects into account and yields decent results for the simulation of a NACA 64A-110 airfoil. To address the shocks in the domain, a finite-volume-based shock capturing method was implemented in FLEXI, which is validated here using the simulation of a linear compressor cascade at supersonic flow conditions, where the method is demonstrated to yield efficient, robust and accurate results. Lastly, we present the TensorFlow-Fortran-Binding (TFFB) as an easy-to-use library to deploy trained machine learning models in Fortran solvers such as FLEXI.
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Venue: arXiv cs.LG 2023
Year: 2023

Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. Additionally, performing inference at runtime requires non-trivial coupling of ML framework libraries with simulation codes. This work offers a solution to both limitations by simplifying this coupling and enabling in situ training and inference workflows on heterogeneous clusters. Leveraging SmartSim, the presented framework deploys a database to store data and ML models in memory, thus circumventing the file system. On the Polaris supercomputer, we demonstrate perfect scaling efficiency to the full machine size of the data transfer and inference costs thanks to a novel co-located deployment of the database. Moreover, we train an autoencoder in situ from a turbulent flow simulation, showing that the framework overhead is negligible relative to a solver time step and training epoch.
Impacts and Risk of Generative AI Technology on Cyber Defense
Venue: arXiv cs.CR 2023
Year: 2023

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.
SQ Lower Bounds for Learning Bounded Covariance GMMs
Venue: arXiv cs.LG 2023
Year: 2023

We study the complexity of learning mixtures of separated Gaussians with common unknown bounded covariance matrix. Specifically, we focus on learning Gaussian mixture models (GMMs) on $\mathbb{R}^d$ of the form $P= \sum_{i=1}^k w_i \mathcal{N}(\boldsymbol μ_i,\mathbf Σ_i)$, where $\mathbf Σ_i = \mathbf Σ\preceq \mathbf I$ and $\min_{i \neq j} \| \boldsymbol μ_i - \boldsymbol μ_j\|_2 \geq k^ε$ for some $ε>0$. Known learning algorithms for this family of GMMs have complexity $(dk)^{O(1/ε)}$. In this work, we prove that any Statistical Query (SQ) algorithm for this problem requires complexity at least $d^{Ω(1/ε)}$. In the special case where the separation is on the order of $k^{1/2}$, we additionally obtain fine-grained SQ lower bounds with the correct exponent. Our SQ lower bounds imply similar lower bounds for low-degree polynomial tests. Conceptually, our results provide evidence that known algorithms for this problem are nearly best possible.